Iterative Learning Control for Non-Linear Discrete-Time Systems with Iterative Varying Reference Trajectory and Varying Trail Lengths Under Random Initial State Shifts

Under random initial state shifts, this study proposes an iterative learning control (ILC) algorithm for non-linear discrete-time system with varying trail lengths. The presented ILC method can drive the non-linear discrete-time system track the iteratively variable the desired trajectory. Under the assumption that the initial state shifts from iteration to iteration, the modified tracking error of ILC can be converged within a bounded range along iteration axis. As the number of iteration goes to infinity, the modified tracking error of ILC can be controlled to zero when the desired trajectory and initial state are fixed. We illustrate the efficacy of the suggested ILC law through two examples.